Book Image

Time Series Analysis on AWS

By : Michaël Hoarau
Book Image

Time Series Analysis on AWS

By: Michaël Hoarau

Overview of this book

Being a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes. The book begins with Amazon Forecast, where you’ll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You’ll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you’ll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data. By the end of this AWS book, you’ll have understood how to use the three AWS AI services effectively to perform time series analysis.
Table of Contents (20 chapters)
1
Section 1: Analyzing Time Series and Delivering Highly Accurate Forecasts with Amazon Forecast
9
Section 2: Detecting Abnormal Behavior in Multivariate Time Series with Amazon Lookout for Equipment
15
Section 3: Detecting Anomalies in Business Metrics with Amazon Lookout for Metrics

Choosing a good data split between training and evaluation

When you're choosing a data split between your training and evaluation periods, you need to consider the following constraints or recommendations:

  • The first constraint to consider is the requirement to have at least 90 days in the training range. At the time of writing, Amazon Lookout for Equipment considers that it needs at least this period to model the normal operating behavior of a piece of industrial equipment. This physical behavior is independent of the granularity at which the sensor data is collected.
  • Ideally, the training range should include all the normal operating behaviors of your process or equipment. If a new behavior is only seen during the evaluation range, then there will be a high chance that Amazon Lookout for Equipment will flag it as an anomaly.

    Important Note

    Make sure that you don't have severe level shifts in some of your sensors (for instance, sensors that stopped working over...